Source localization is a challenging issue for multisensor multitarget detection, tracking and estimation problems in wireless distributed sensor networks. In this paper, a novel source localization method, called passive source localization using power spectral analysis and decision fusion in wireless distributed sensor networks is presented. This includes an energy decay model for acoustic signals. The new method is computationally efficient and requires less bandwidth compared with current methods by making localization decisions at individual nodes and performing decision fusion at the manager node. This eliminates the requirement of sophisticated synchronization. A simulation of the proposed method is performed using different numbers of sources and sensor nodes. Simulation results confirmed the improved performance of this method under ideal and noisy conditions

Dooley, L. S.

Karmakar, G. C.

Rahman, M. Z.

English

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Passive source localization using power spectral analysis and decision fusion in wireless distributed sensor networks

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Open Research OnlineThe Open University’s repository of research publicationsand other research outputsPassive source localization using power spectralanalysis and decision fusion in wireless distributedsensor networksConference or Workshop ItemHow to cite:Rahman, M. Z.; Karmakar, G. C. and Dooley, L. S. (2005). Passive source localization using power spectralanalysis and decision fusion in wireless distributed sensor networks. In: IEEE International Conference on InformationTechnology, Coding and Computing (ITCC ’05), 4-6 Apr 2005, Las Vegas.For guidance on citations see FAQs.c© [not recorded]Version: [not recorded]Link(s) to article on publisher’s website:http://dx.doi.org/doi:10.1109/ITCC.2005.225Copyright and Moral Rights for the articles on this site are retained by the individual authors and/or other copyrightowners. For more information on Open Research Online’s data policy on reuse of materials please consult the policiespage.oro.open.ac.ukPassive Source Localization using Power Spectral Analysis and Decision Fusionin Wireless Distributed Sensor NetworksM. Ziaur Rahman, Gour C. Karmakar and Laurence S. DooleyGSCIT, Monash University, Churchill Vic 3842, Australia{Ziaur.Rahman, Gour.Karmakar, Laurence.Dooley}@infotech.monash.edu.auAbstractSource localization is a challenging issue for multisensormultitarget detection, tracking and estimation problems inwireless distributed sensor networks. In this paper, a novelsource localization method, called passive source localiza-tion using power spectral analysis and decision fusion inwireless distributed sensor networks is presented. This in-cludes an energy decay model for acoustic signals. The newmethod is computationally efﬁcient and requires less band-width compared with current methods by making localiza-tion decisions at individual nodes and performing decisionfusion at the manager node. This eliminates the require-ment of sophisticated synchronization. A simulation of theproposed method is performed using different numbers ofsources and sensor nodes. Simulation results conﬁrmed theimproved performance of this method under ideal and noisyconditions.1 IntroductionWireless Distributed Sensor Networks (WDSN) are apromising research topic in the literature due to their hugepotential in different application areas from the monitor-ing to manipulation of physical world in a seamless fash-ion [6], [5], [8], [4]. WDSN comprise cheap nodes thathave limited sensing, communication and computation ca-pabilities. There are also some nodes designated as controlnodes having powerful computational and communicationability. Each WDSN node can have different types of sen-sors such as acoustic, seismic and image. The fusion ofdata collected through sensor nodes to improve decision isa major research issue for WDSN.Source localization is the most fundamental part of mul-tisensor multitarget detection. The objective is to estimatethe position of a source within the region covered by aWDSN.Localization methods depend on three types of physi-cal variables measured by or derived from, sensor read-ings: time delay of arrival (TDOA)[3], direction of ar-rival (DOA)[7] and received signal strength[9]. The sourcelocalization problem is solved by TDOA and DOA forWDSN using the same philosophy for localization as usedin RADAR applications. This is not a suitable approachfor WDSN since RADAR and WDSN nodes have funda-mental differences in their sensing quality, computationalability and communication framework. The basic advan-tage of WDSN is that it has sufﬁcient number of cheapand redundancy nodes that are feasible to deploy for track-ing the source in close proximity. The critical issues inthe application of WDSN to source localization are to copewith the efﬁcient collaborative management speciﬁcally fordecision fusion from readings of non-sophisticated nodeshaving scarce power, computational and communication re-sources. Only a selective subset of nodes should be allowedto communicate with the control node to reduce power con-sumption of individual nodes and the bandwidth overheadof the network.A maximum likelihood(ML) source localization methodusing acoustic energy measurements from individual sen-sor in WDSN was presented in [9]. The ML method hasseveral limitations for applications in WDSN. Speciﬁcally,it is sensitive to the parameter perturbation and computa-tionally very expensive for multitarget location estimation[10]. These limitations made the ML estimation unsuitablefor WDSN.Particle ﬁlters were applied in [10] to overcome the lim-itations of ML estimation, by making an assumption aboutsource dynamics such as source velocity and acceleration,which is not suitable for enemy/adverse situation surveil-lance.To address these issues, this paper presents a novelmethod for source localization namely, passive source lo-calization using power spectral analysis and decision fusionin wireless distributed sensor networks. The motivation be-hind using power spectral density (PSD) for source localiza-tion is that any rotating object emits its signal at a particularfrequency. Analyzing this feature using PSD for a sampleof a speciﬁc source helps identify the source.Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC’05) 0-7695-2315-3/05 $ 20.00 IEEEThe probabilistic localization is made from PSD analysisat each node. Statistical decision fusion of these estimatesis made at a control node. The issue of accurate synchro-nization among the sensors is also resolved by their coarsetime series data collection.The paper is organized as follows: Section 2 gives a shortoverview on the PSD and its signiﬁcance for source local-ization in WDSN. Section 3 provides a detailed descrip-tion of the theoretical underpinning of the proposed method.Simulation results are described in Section 4, and ﬁnallysome conclusions are presented in Section 5.2 Power Spectral Density AnalysisThe energy ξ(t), of an arbitrary time varying signal x(t)is represented in both time and frequency domains by Par-seval’s theorem as,ξ(t) =∫ ∞∞x1(t)x2(t)dt =∫ ∞∞X1(f)X2(f)df (1)where X(f) is equivalent Fourier transform representationof x(t). Setting x1(t) = x2(t), we get,ξ(t) =∫ ∞∞x2(t)dt =∫ ∞∞|X(f)|2df (2)|X(f)|2 is known as the power spectral density function.The Wiener-Khintchine theorem proves that the spectraldensity P (f) = |X(f)|2, of a stationary random processis just the Fourier transform of the autocorrelation functionRxx(τ) and is represented by the pair:P (f) =∫ ∞∞Rxx(τ)e−jωτdτ (3)andRxx(τ) =∫ ∞∞P (f)ejωτdf (4)The periodogram is a commonly used PSD estimation tech-nique using (3) [1]. Transforming from time domain to fre-quency domain for source localization is an integral partof signal analysis and hence periodogram for PSD estima-tion forms the fundamental basis for the proposed new algo-rithm. There are other methods for PSD estimation. Since,we are focusing on use of power spectral analysis for sourcelocalization we have chosen the simplest one to implementi.e. periodogram.3 Source Localization using Power SpectralAnalysis and Decision FusionAs alluded in Section 1, source localization using powerspectral analysis and decision fusion is motivated by energybased source estimation algorithms [10], [12]. Using theformula for acoustic signal attenuation, it is possible to esti-mate the source location using redundant reading at differ-ent, known sensor locations.Figure 1. Block Diagram of Proposed SourceLocalization using Power Spectral Analysisand Decision Fusion3.1 Feature IdentiﬁcationAs shown in Fig. 1, feature identiﬁcation is one of theprocessing steps of source localization using power spectralanalysis and decision fusion method. This can be obtainedin different ways: - (1). collecting sampling data and (2).for a given set of features for a particular source. Samplingdata can be collected for featured sources at ideal noise freecondition at very short distance. The periodogram of a spe-ciﬁc source signal at this condition gives the PSD estimationof the sampled data. The obtained PSD is then processed forpower peak detection and corresponding frequency ﬁnding.The obtained peak power level and corresponding averagefrequency forms power-frequency pair [P, f ], where P rep-resents the power level and f represents corresponding fre-quencies. This [P, f ] pair denotes the characteristic featurefor the speciﬁc source.3.2 Realtime Source LocalizationThe realtime source localization is the heart of the pro-posed source localization using the power spectral analysisand decision fusion method shown in Fig. 1. For the sakeof the clarity , it is assumed that there are M sources andN sensors in a WDSN ﬁeld. A node in the WDSN is mod-eled by [xi, yi, σi], where (xi, yi) denotes the node positionand σi its associated standard deviation of the estimationprocess for source localization. σi can be varied based onsensor quality measure or it can be estimated by an adaptiveprocess. Assume that the signal received by the ith node isthe sum of attenuated signals emitted from each of these Msources. Thus, the signal energy received by the ith nodeover a time interval t, denoted by yi(t), is expressed as fol-lows:Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC’05) 0-7695-2315-3/05 $ 20.00 IEEEyi(t) = γiM∑m=1sm(t)‖ρm(t)− ri‖2 + εi(t) (5)where εi(t) is a perturbation term that summarizes thenet effect of background additive noise and the parametermodeling error. γi and ri are the gain factor and location ofthe ith sensor respectively. sm(t) and ρm(t) are the energyemitted by the mth source and its location during the tthtime interval. The probability distribution of εi(t) has beenshown to be independent and identically distributed (i.i.d.)Gaussian random variable when the time period T for aver-aging the energy is sufﬁciently large [9].Each node collects data for a speciﬁc T time period. ThePSD estimation for ith node Pi(f) is made over the periodT for each sampling time. The recent collected data over theperiod of T is used for PSD and hence location estimation.Sources are detected from the obtained PSD by matchingwith characteristic power-frequency pair. Due to the con-sideration of noise, the matching for particular frequency fis spread over within the range of f±df frequencies, where±df is the width of the variation of a particular frequencyf caused by noise perturbation term εi(t). The peak powerwithin f ± df is used as detected power level.For the jth frequency component, ρˆijm represents the ra-dial distance estimation of mth source from ith node usingcalculated PSD and characteristic power-frequency pair forindividual sources, where j = 1..k and k is the number ofdistinct features present in the power-frequency pair. Theﬁnal estimation of ρim is found by averaging all ρˆijm’s. Thisis derived from (5), considering the fact perturbation termεi(t) is eliminated by PSD estimation and expressed as fol-lows:ρim =1kk∑j=1(γiSPmRP jm) 12(6)where SPm denotes the signal power level at origin, RP jmdenotes received signal power level for mth source usingjth frequency feature component and γi is a constant.The obtained ρim is transmitted to the control node fordecision fusion. A 3D probability density function pim(x)having mean ρim and standard deviation σi is constructedfor each node and source m at the control node throughproper normalization. For location estimation from all sup-ported data, all the probabilistic density functions from allnodes need to be fused together at the control node. There-fore, all probabilistic measures are fused together usingweighting factor wi for ith node. This process generatesan integrated 3D probability density function for the ﬁnaldecision making. This process for source m is formulatedby the following equation.pm(x) =N∑i=1wipmi (x) (7)To localize a source from this pm(x), a peak detectiontechnique is employed in the pm(x). The locations of thedetected peaks having probability estimation greater than apreset threshold h corresponds to detected source locations.4 Simulation ResultsThe proposed algorithm was simulated using Matlab6.5.1 (The Mathworks Inc.). For the simulation, threesources having characteristic frequencies at 200, 300 and400 Hz and signal strength at source of 70, 75 and 80 dBrespectively were used. The sound level and the dominantfrequencies that have been used are in conformance withthe power and frequency levels generated by conventionaland military vehicles [11], [2]. The values of sampling fre-quency and σi were taken as 1000 Hz and 2 respectively.The weighting factorswi are considered same for each nodeand set to 15 . The original node and source positions and thedetected source positions are shown in Fig. 2.−100 −80 −60 −40 −20 0 20 40 60 80 100−100−80−60−40−20020406080100x−position (m)y−position (m)   Node−1   Node−2   Node−3   Node−4   Node−5   Source−1   Source−2Node Location Source LocationEstimated Source LocationFigure 2. Ground Truth and Simulation Re-sults for Three Sources0 0.2 0.4 0.6 0.8 1−40−30−20−10010203040Normalized Frequency  (×π rad/sample)Power Spectral Density (dB/ rad/sample)Figure 3. PSD Estimation at node 1For better understanding of how a power level peak beinggenerated for a particular source having a distinct frequencyProceedings of the International Conference on Information Technology: Coding and Computing (ITCC’05) 0-7695-2315-3/05 $ 20.00 IEEEin the PSD, a representative sample of the estimated PSDfrom the simulation for node 1 is shown in Fig. 3. It is clearfrom this ﬁgure that representative frequencies for the threesources used here, generate distinct peaks in the PSD. Thesepeak power levels provide the radial source distance by theuse of the attenuation model described in Section 3.The contribution of source 1 location estimation fromindividual node are fused together at the control node asshown in Fig. 4(a). The resulting 3D probabilistic estima-tion plot is shown in Fig. 4(b). The same results for source2 are shown in Fig. 5.Figure 4. Source Localization Result forSource 1, a) A 2D radial basis location esti-mation for different nodes, b) A 3D probabilityspace for source localizationFrom the obtained results in Fig. 2, it is shown that theproposed localization method can locate sources within theerror range of 1 m. Also, due to radial estimation, it hasbeen found that there are circular regions of a source lo-cation generated by different nodes. Fig. 4 and 5 showhow the redundant information from the nodes play a roleto make a decision for a source localization. The individualdecisions from nodes are aggregated using (7). At the inter-section of the circular regions the decision is strengthenedto ultimately predict the source location estimation shownin Fig. 4 and 5 for Source 1 and 2 respectively. It is evidentFigure 5. Source Localization Result forSource 2, a) A 2D radial basis location esti-mation for different nodes, b) A 3D probabilityspace for source localizationfrom the obtained results that the decision fusion using theredundant information of the sensor nodes plays a vital roleto make a decision for source localization.Another experiment was conducted using the sameground truth information including addition of AdditiveWhite Gaussian Noise(AWGN) of 5 dB in order to simulatethis method for testing its performance in a real-life context.Simulation showed exactly the same result as the AWGN isﬁltered out in frequency domain power spectral density es-timation. It is clearly evident that the location estimation ofthe proposed method is not sensitive to AWGN and hence,suitable to determine the location of the source in a real-lifeenvironment.5 ConclusionsIn this paper, a novel energy based source localizationusing power spectral analysis and decision fusion methodin WDSN has been introduced. A simulation has been con-ducted for multitarget location estimation for different lev-els of noise perturbation. The results have shown the ac-curate location estimation of the proposed method, whichProceedings of the International Conference on Information Technology: Coding and Computing (ITCC’05) 0-7695-2315-3/05 $ 20.00 IEEEused the periodogram for PSD estimation. Other existingmethods for PSD estimation could also be investigated andcompared in future work.References[1] P. Bloomﬁeld. Fourier Analysis of Time Series: An Intro-duction. John Wiley & Sons, 2nd edition, 2000.[2] E. Buchta. Trafﬁc noise associated with military trainingareas. In The 32nd International Congress and Expositionon Noise Control Engineering, Jeju International Conven-tion Center, Seogwipo, Korea, AUG 2003.[3] J. C. Chen, R. E. Hudson, and K. yao. Maximum-likelihoodsource localization and unknown sensor location estimationfor wideband signals in the near-ﬁeld. 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In Interna-tional Conference on Acoustics, Speech, and Signal Process-ing (ICASSP 2001), pages 3001–3004, Salt Lake City, Utah,May 2001.[8] G. J. Pottie and W. J. Kaiser. Wireless integrated networksensors. Communications of the ACM, 43(5):51–58, 2000.[9] X. Sheng and Y. H. Hu. Energy based acoustic source local-ization. Information Processing in Sensor Networks, pages285–300, 2003.[10] X. Sheng and Y. H. Hu. Sequential acoustic energy basedsource localization using particle ﬁlter in a distributed sensornetwork. In International Conference on Acoustics, Speech,and Signal Processing, pages 972–975, 2004.[11] USACHPPM. Noise Levels of Common Army Equipment.http://chppm-www.apgea.army.mil/hcp/NoiseLevels.aspx,Retrieved 28 NOV, 2004.[12] Y. Zou and K. Chakrabarty. Energy aware target localizationin wireless sensor networks. 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Passive source localization using power spectral analysis and decision fusion in wireless distributed sensor networks

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